Regression

Linear Models in Statistics

  • N. H. Bingham
  • John M. Fry

Part of the Springer Undergraduate Mathematics Series book series (SUMS)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. N. H. Bingham, John M. Fry
    Pages 1-32
  3. N. H. Bingham, John M. Fry
    Pages 33-59
  4. N. H. Bingham, John M. Fry
    Pages 61-97
  5. N. H. Bingham, John M. Fry
    Pages 99-127
  6. N. H. Bingham, John M. Fry
    Pages 129-148
  7. N. H. Bingham, John M. Fry
    Pages 149-162
  8. N. H. Bingham, John M. Fry
    Pages 163-180
  9. N. H. Bingham, John M. Fry
    Pages 181-201
  10. N. H. Bingham, John M. Fry
    Pages 203-225
  11. Back Matter
    Pages 227-284

About this book

Introduction

Regression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error. The subject is inherently two- or higher- dimensional, thus an understanding of Statistics in one dimension is essential.

Regression: Linear Models in Statistics fills the gap between introductory statistical theory and more specialist sources of information. In doing so, it provides the reader with a number of worked examples, and exercises with full solutions.

The book begins with simple linear regression (one predictor variable), and analysis of variance (ANOVA), and then further explores the area through inclusion of topics such as multiple linear regression (several predictor variables) and analysis of covariance (ANCOVA). The book concludes with special topics such as non-parametric regression and mixed models, time series, spatial processes and design of experiments.

Aimed at 2nd and 3rd year undergraduates studying Statistics, Regression: Linear Models in Statistics requires a basic knowledge of (one-dimensional) Statistics, as well as Probability and standard Linear Algebra. Possible companions include John Haigh’s Probability Models, and T. S. Blyth & E.F. Robertsons’ Basic Linear Algebra and Further Linear Algebra.

Keywords

ANOVA Generalized linear model STATISTICA Time series analysis of covariance analysis of variance general linear model linear regression regression

Authors and affiliations

  • N. H. Bingham
    • 1
  • John M. Fry
    • 2
  1. 1.Imperial College LondonLondonUnited Kingdom
  2. 2.University of East LondonLondonUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84882-969-5
  • Copyright Information Springer-Verlag London Limited 2010
  • Publisher Name Springer, London
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-84882-968-8
  • Online ISBN 978-1-84882-969-5
  • Series Print ISSN 1615-2085
  • About this book